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Micro- and macroeconomic factors, along with shifting customer demand trends, place continuous demands on supply chain networks. Companies must enhance their efficiency and agility while proactively refining their supply chain operations to boost performance and satisfy customer needs. Effective supply chain management systems integrate various functions that harmonize resources and business processes. Demand planning is critical in this context, ensuring timely stock replenishment, optimized capacity management, and maximized sales and revenue.
In a prior discussion, we explored how AWS Supply Chain enhances supply chain visibility to bolster resilience. This article focuses on AWS Supply Chain Demand Planning, a specialized demand planning module designed to produce accurate demand forecasts that adapt to market fluctuations and consistently learn from evolving demand patterns and user inputs to enhance forecast precision.
The Demand Planning Process
Accurate demand forecasting is crucial. Inaccurate forecasts can lead to inventory discrepancies, resulting in overstock situations or stockouts, higher costs, lost sales opportunities, and potentially lower customer satisfaction. For instance, overestimating demand can lead to surplus inventory, diminishing cash flow and increasing storage expenses. Conversely, underestimating demand results in stock shortages, negatively impacting customer experience and satisfaction and potentially leading to customer attrition.
Demand planning is essential for organizational success. It determines how, when, and where resources are allocated to meet customer demands without overextending resources. Through systematic evaluation, forecasting, collaboration, and regular reassessment, it shapes supply chain strategies and operational efficacy. The typical demand planning process involves several steps categorized as follows:
- Data Integration: This forms the backbone of demand planning. It entails collecting historical sales data, current order data, inventory levels, and other relevant metrics. Data sources may include enterprise resource planning (ERP) systems, customer relationship management (CRM) tools, and external market intelligence reports. Integrating diverse data streams is crucial for a comprehensive view of demand factors.
- Forecasting: Once data is collected and integrated, statistical models and algorithms are utilized to predict future demand. This phase combines empirical data with industry insights and is where machine learning (ML) can significantly enhance accuracy.
- Collaboration: Successful demand planning necessitates communication and collaboration across departments, including sales, marketing, finance, and operations. By gathering insights from various teams, organizations can align statistical forecasts with market intelligence, promotional strategies, and strategic objectives. This collaborative effort ensures that the forecast reflects a consensus viewpoint, thereby improving its accuracy and acceptance.
- Continuous Review and Adjustment: Demand planning is an ongoing process. As actual sales data is received, it is compared against forecasts to identify discrepancies. These differences are analyzed to refine forecasting models and adjust future predictions. Regularly reviewing and updating the demand plan ensures it remains relevant and aligned with the latest market conditions and internal strategies.
The interdependence of these four categories is vital: without accurate data, forecasts falter; without collaboration, forecasts lack depth; and without continuous review, even the best forecasts become outdated. This interconnectedness keeps the process dynamic, precise, and pertinent, reflecting market realities and business aspirations.
Advantages of AWS Supply Chain
AWS Supply Chain offers several advantages over traditional demand planning:
- Automation: Demand Planning streamlines numerous non-value-added manual tasks, such as data entry, calculations, and adjustments. This leads to quicker demand forecast generation and minimizes errors associated with manual processes.
- Leveraging ML: Machine learning examines historical sales and real-time data (such as open orders) to produce forecasts and adapt models for improved accuracy. This not only enhances forecast precision but also mitigates the risks of inventory shortages (stockouts) or surpluses (excess inventory). AWS Supply Chain also employs ML to detect lead-time variability, bolstering supply planning accuracy.
- Efficient Collaboration: Integrated collaboration features facilitate consensus-building among team members. This enhances coordination, accelerates decision-making, and reduces error risks.
The subsequent section elaborates on how AWS Supply Chain supports the demand planning journey, focusing on the initial three key phases of the process (data integration, forecasting, and collaboration) and providing a comprehensive guide for new users. These steps will help you set up AWS Supply Chain seamlessly, leverage its advanced functionalities, and transform your demand planning strategy. Future articles will address the fourth phase, continuous review and adjustment, detailing its integration with AWS Supply Chain based on industry best practices.
AWS Supply Chain Demand Planning Prerequisites
To get started, you will need an AWS account. If you do not have one, follow the account creation process described in How do I create and activate a new AWS account? Additionally, an AWS Supply Chain account is required. If you are not a current customer, explore AWS Supply Chain to learn more and initiate the process.
Setting Up Demand Planning
The first step involves ingesting data into the AWS Supply Chain data lake. The data lake utilizes ML models to comprehend, extract, and transform disparate, incompatible data into a unified data model. As an administrator, populate the AWS Supply Chain Data Lake with the necessary data entities for demand planning forecasts. The Data fields required by AWS Supply Chain applications in the AWS Supply Chain User Guide provides a complete list of required fields for forecast generation. For enhanced accuracy, ensure that optional entities in the dataset are filled out, as described in the Data fields optional for AWS Supply Chain applications.
Once data ingestion is complete, verify user permissions. As an admin, you can add the desired number of users for Demand Planning and manage their permissions. New user invitations will be sent after permissions are established. At the bottom of the screen, admins can choose from four role-based permission levels: Admin, Data Analyst, Inventory Manager, or Planner.
After selecting user permission roles, navigate to AWS Supply Chain in the left pane and select “Get Started” to launch the Demand Planning module.
The next step involves specifying the forecast generation timeframe (the planning horizon). Input the forecast interval and the duration for which you wish the application to generate a forecast plan. For example, if you want a monthly forecast for the next six months, select “Monthly” for the time interval and enter “6” for the period.
You will then configure the forecast granularity based on your preferences or requirements. On this screen, select the level at which you wish to forecast by choosing the hierarchy attributes for site, channel, and customer.
With these steps completed, you are well on your way to an efficient demand planning process. For further insights on leadership and effective management practices, consider checking out Women Who Lead and SHRM’s insights on leadership challenges. Additionally, for those preparing for interviews, Glassdoor provides excellent resources.